The needs of persons with lupus and health care providers: a qualitative study aimed toward the development of the Lupus Interactive Navigator™
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus is an inflammatory autoimmune disease associated with high morbidity and unacceptable mortality. A major challenge for persons with lupus is coping with their illness and complex care. Our objective was to identify the informational and resource needs of persons with lupus, rheumatologists, and allied health professionals treating lupus. Our findings will be applied toward the development of an innovative web-based technology, the Lupus Interactive Navigator (LIN™), to facilitate and support engagement and self-management for persons with lupus. METHODS: Eight focus groups were conducted: four groups of persons with lupus (n=29), three groups of rheumatologists (n=20), and one group of allied health professionals (n=8). The groups were held in British Columbia, Ontario, and Quebec. All sessions were audio-recorded and transcribed verbatim. Qualitative analysis was performed using grounded theory. The transcripts were reviewed independently and coded by the moderator and co-moderator using 1) qualitative data analysis software developed by Provalis Research, Montreal, Canada, and 2) manual coding. RESULTS: Four main themes emerged: 1) specific information and resource needs; 2) barriers to engagement in health care; 3) facilitators of engagement in health care; and 4) tools identified as helpful for the self-management of lupus. CONCLUSION: These findings will help guide the scope of LIN™ with relevant information topics and specific tools that will be most helpful to the diverse needs of persons with lupus and their health care providers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".